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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 901 records · Page 50

Handling Qualities Evaluations of Low Complexity Model Reference Adaptive Controllers for Reduced Pitch and Roll Damping Scenarios

National Aeronautics and Space Administration (NASA) researchers have conducted a series of flight experiments designed to study the effects of varying levels of adaptive controller complexity on the performance and handling qualities of an aircraft under various simulated failure or damage conditions. A baseline, nonlinear dynamic inversion controller was augmented with three variations of a model reference adaptive control design. The simplest design consisted of a single adaptive parameter in each of the pitch and roll axes computed using a basic gradient-based update law. A second design was built upon the first by increasing the complexity of the update law. The third and most complex design added an additional adaptive parameter to each axis. Flight tests were conducted using NASA s Full-scale Advanced Systems Testbed, a highly modified F-18 aircraft that contains a research flight control system capable of housing advanced flight controls experiments. Each controller was evaluated against a suite of simulated failures and damage ranging from destabilization of the pitch and roll axes to significant coupling between the axes. Two pilots evaluated the three adaptive controllers as well as the non-adaptive baseline controller in a variety of dynamic maneuvers and precision flying tasks designed to uncover potential deficiencies in the handling qualities of the aircraft, and adverse interactions between the pilot and the adaptive controllers. The work was completed as part of the Integrated Resilient Aircraft Control Project under NASA s Aviation Safety Program.

Hanson, Curt↗

Evaluation of Full Reynolds Stress Turbulence Models in FUN3D for Axisymmetric Jet Flow

Two full seven-equation turbulence models have been implemented into the FUN3D code to evaluate their ability to improve the computation of challenging flows encountered in aerospace propulsion, including mixing flows. These models are the SSG/LRR and Wilcox full second-moment Reynolds stress models. They solve equations for the six components of the Reynolds stress and a seventh equation for the mixing length. Two standard eddy viscosity models are also evaluated for comparison, the Spalart-Allmaras (SA) one-equation model and the Menter Shear Stress Transport (SST-V) two-equation turbulence model. Flow through an axisymmetric reference nozzle is examined at three flow conditions: subsonic unheated, subsonic heated, and near sonic unheated. Centerline profiles of velocity and turbulent kinetic energy and radial profiles of velocity, turbulent kinetic energy and turbulent stresses are examined. characteristics, no significant changes in the downstream flow behavior compared to the baseline case are observed. Furthermore, the total power consumed by the fans for different incoming flow conditions also remain marginally the same. It is hoped that the results, albeit obtained at very low speeds. would serve as a database for this technologically interesting flow field that has not been explored adequately before.

computational fluid dynamics↗

Updating the BNB Flux Prediction at SBND

Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux uncertainties contribute significantly to the total systematic uncertainties seen in modern accelerator neutrino measurements such as cross sections, oscillations, and BSM studies.. For over a decade, experiments utilizing Fermilab’s Booster Neutrino Beam (BNB) have relied on the 2009 MiniBooNE flux prediction. However, the high-statistics era of the Short-Baseline Neutrino (SBN) program, with both SBND and ICARUS now operating, demands a modernized flux model and framework. While SBND is using the MiniBooNE flux model for its Generation 1 analyses, including many upcoming cross section measurements, future work will be based on a new flux model. In this talk, the ongoing work towards this new model will be outlined, including a new simulation framework (G4BNB), a new evaluation of model parameters from hadron scattering data, and a new framework for evaluating systematic uncertainties (BNBFP). Additionally, expansions to the flux model to include BSM contributions from neutral mesons will be discussed, as well as the PRISM capabilities of SBND to observe a wide range of off axis angles of the neutrino beam.

Paton, Josephine [Fermilab]↗

Updating the BNB Flux Prediction at SBND

Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux uncertainties contribute significantly to the total systematic uncertainties seen in modern accelerator neutrino measurements such as cross sections, oscillations, and BSM studies.. For over a decade, experiments utilizing Fermilab’s Booster Neutrino Beam (BNB) have relied on the 2009 MiniBooNE flux prediction. However, the high-statistics era of the Short-Baseline Neutrino (SBN) program, with both SBND and ICARUS now operating, demands a modernized flux model and framework. While SBND is using the MiniBooNE flux model for its Generation 1 analyses, including many upcoming cross section measurements, future work will be based on a new flux model. In this talk, the ongoing work towards this new model will be outlined, including a new simulation framework (G4BNB), a new evaluation of model parameters from hadron scattering data, and a new framework for evaluating systematic uncertainties (BNBFP). Additionally, expansions to the flux model to include BSM contributions from neutral mesons will be discussed, as well as the PRISM capabilities of SBND to observe a wide range of off axis angles of the neutrino beam.

Paton, Josephine [Fermilab]↗

A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System

In 2018, as result of a recommendation from the National Academies, NASA began to prototype an In-Time Aviation Safety Management System(IASMS). The purpose of the IASMS is to enable innovative aviation operations and greater heterogeneity of the overall National Airspace (NAS) by automating much of the safety monitoring, assessment, and risk and hazard mitigation functionspresent in today’s Safety Management Systems (SMS). NASA has worked together with early industry collaborators to understand how such a system might work and has published several early Concepts of Operation (ConOps) and other technical memoranda that illustrate the primary considerations for selected aviation domains. The shift from an SMS to an IASMS is predicated on several assumptions, including: 1.) automating safety functions will decrease the amount of time necessary for risk and hazard identification and analysis, making it more likely that safety concerns are understood ‘in-time’ to mitigate them, and 2.) an IASMS will allow easier tailoring of safety management processes to the particular risks and hazards inherent to that aviation operation. In this paper, we begin to validate these assumptions through the use of Model-Based Systems Engineering (MBSE).

In-time Aviation Safety Management System↗

A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System

In 2018, as result of a recommendation from the National Academies, NASA began to prototype an In-Time Aviation Safety Management System (IASMS). The purpose of the IASMS is to enable innovative aviation operations and greater heterogeneity of the overall National Airspace (NAS) by automating much of the safety monitoring, assessment, and risk and hazard mitigation function present in today’s Safety Management Systems (SMS). NASA has worked together with early industry collaborators to understand how such a system might work and has published several early Concepts of Operation (ConOps) and other technical memoranda that illustrate the primary considerations for selected aviation domains. The shift from an SMS to an IASMS is predicated on several assumptions, including: 1.) automating safety functions will decrease the amount of time necessary for risk and hazard identification and analysis, making it more likely that safety concerns are understood ‘in-time’ to mitigate them, and 2.) an IASMS will allow easier tailoring of safety management processes to the particular risks and hazards inherent to that aviation operation. In this paper, we begin to validate these assumptions through the use of Model-Based Systems Engineering (MBSE).

IASMS↗

Evaluation of 2 1-D cloud models for the analysis of VAS soundings

Evaluation of the satellite Visual Infrared Spin Scan Radiometer Atmospheric Sounder (VISSR) has begun to document several of its critical shortcomings as far as numerical cloud models are concerned: excessive smoothing of thermal inversions; imprecise measurement of boundary layer moisture; and tendency to exaggerate atmospheric stability. The sensitivity of 1-D cloud models to their required inputs is stressed with special attention to those parameters obtained from atmospheric soundings taken by the VAS or rawinsonde. In addition to performing model experiments using temperature and moisture profiles having the general characteristics of VAS soundings, standard input sensitivity tests were made and 1-D model performance was compared with observations and the results of a 2-D model experiment using AVE/VAS data (Atmospheric Variability Experiment). Although very encouraging, the results are not sufficient to make any specific conclusions. In general, the VAS soundings are likely to be inadequate to provide the cloud base (and subcloud layer) information needed for inputs to current cumulus models. Above cloud base, the tendency to exaggerate the stability of the atmosphere requires solution before meaningful model experiments are run.

Emmitt, G. D.↗

Model Card for WaveDenoiser

This study used STEAD to train and evaluate this model because STEAD is among the best benchmark datasets available for local to regional data. STEAD is a global dataset with over 1 million 60 s long seismic waveforms that originated from approximately 450,000 earthquakes and background noise captured by more than 2,500 seismic stations. Each waveform in STEAD was attached with metadata such as earthquake locations, station locations, and signal arrival times when available

58 GEOSCIENCES↗

An experimental test of plant canopy reflectance models on cotton

Extensive data on the plant parameters necessary to evaluate any model are presented for a cotton crop. The variation of the bidirectional reflectance function with observer altitude, observer azimuth, and sun altitude angle is presented for a high density cotton crop having leaf index of 19. A comparison with the quantitative behavior obtained from the Suits model is accomplished in the wavelength region from 400 nm to 1050 nm.

Lemaster, E. W.↗

A real time Pegasus propulsion system model for VSTOL piloted simulation evaluation

A Pegasus-Harrier propulsion system is selected as a baseline for developing mathematical modeling and simulation techniques for VSTOL. Initially, static and dynamic propulsion system characteristics are modeled in detail to form a nonlinear aerothermodynamic digital computer simulation of a Pegasus engine. From this high fidelity simulation, a real-time propulsion model is formulated by applying a piecewise linear state variable methodology. A hydromechanical and water injection control system is also simulated. It is noted that the real-time dynamic model includes the detail and flexibility required for evaluating critical control parameters and propulsion component limits over a limited flight envelope.

Mihaloew, J. R.↗

Modeling and Experimental Demonstration of Flux Spreading in Light Trapping Planar-Cavity Solar Enclosed Particle Receivers

This study experimentally validates and numerically models the flux-spreading effect in a light-trapping planar-cavity solar receiver) for particle-based concentrating solar power systems. The receiver's shallow cavity with vertical planar walls redistributes concentrated solar flux, reducing peak intensity and achieving uniform heat flux. On-sun tests at National Renewable Energy Laboratory's High-Flux Solar Furnace under flux up to 1500 kW/m2 measured cavity wall temperatures, which were compared with Monte Carlo ray-tracing (SolTrace) and computational numerical simulations. Three angular absorptance models were evaluated: constant absorptance, a Pyromark-based directional model, and a Fresnel-based Cr2O3 model. The Fresnel-derived model showed the best agreement with experiments, achieving high correlation (PC > 0.85), structural similarity (SSIM > 0.98), and signal-to-noise ratios (PSNR > 40 dB), with temperature prediction errors of 1-11%. Results confirm that flux spreading mitigates local overheating and validate the integrated modeling approach, supporting the solar receiver scalability for high-efficiency, high-temperature concentrating solar power applications.

14 SOLAR ENERGY↗

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗

Identification and evaluation of linear damping models in beam vibrations

Sensitive method, identifying effective damping mechanisms, involves comparing experimentally determined ratio of first to second mode magnification factors related to common point on beam. Cluster size has little effect on frequencies of elements, magnification factor decreases with cluster size, and viscous and stress damping are dominant damping mechanisms.

Boers, B. L.↗